{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from ggplot import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `ggplot`\n",
    "`ggplot` is the base layer or object that you use to define\n",
    "the components of your chart (x and y axis, shapes, colors, etc.).\n",
    "You can combine it with layers (or geoms) to make complex graphics\n",
    "with minimal effort. `ggplot` has 2 parameters:\n",
    "\n",
    "- (1) What data is going to be used for your plot _(the data frame being used)_\n",
    "- (2) How that data is going to be displayed _(the aesthetics)_\n",
    "\n",
    "You can add layers to `ggplot` to build up your plot. For the example below we'll start with a `ggplot` that has the `x` aesthetic defined as the `price` column from the `diamonds` dataset. We'll then add a `geom_histogram` to it and then finally add `scale_x_continuous` to customize the x-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "p = ggplot(aes(x='price'), data=diamonds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wma+ZcsabZGxuZGx+5Gz2ZGx+ZGxullK+ZnUk4+Pj2bdvXzZu3Jh77703yXeK6aVLl9LT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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f53ba10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (284507049)>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p + geom_histogram()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jmT9/flavXn3uRwYAAGfonKK4Uqnk/e9/f6rV6ti2Rx99NFdddVWWL1+eRx99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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110167d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (284507049)>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p + geom_histogram() + scale_x_continuous(\"Price ($)\", breaks=[0, 10000, 20000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
